Memanto MCP Server

Persistent semantic memory for any MCP-compatible agent.

This package exposes Memanto's memory primitives — remember, recall, answer, and friends — as Model Context Protocol (MCP) tools so any MCP client (Claude Desktop, Cursor, Windsurf, Cline, Continue, Goose, custom agents, …) can plug into long-term memory in a single config line.

One Moorcheh API key → typed semantic memory across every agent that shares the namespace, with sub-90 ms retrieval, conflict detection, and zero ingestion latency.

---

Install

pip install memanto-mcp

Requires Python 3.10+, memanto>=0.2.13, mcp>=1.2,<2, and a Moorcheh API key (free tier: 100K ops/month).

Quick start (Claude Desktop)

1. Get a Moorcheh API key from the console.
2. Edit claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "memanto": {
      "command": "memanto-mcp",
      "env": {
        "MOORCHEH_API_KEY": "mch_xxxxxxxxxxxxxxxxxx",
        "MEMANTO_DEFAULT_AGENT_ID": "my-assistant"
      }
    }
  }
}

3. Restart Claude Desktop. Ask it to "remember that I prefer concise answers" — then in a brand-new chat tomorrow ask "what do I prefer?".

The first call auto-creates the my-assistant agent and namespace; every subsequent call reuses the same persistent memory.

Quick start (Cursor / Windsurf / Cline / Continue / Goose)

Most clients consume a config file in the standard MCP shape. The same JSON snippet works almost verbatim:

{
  "mcpServers": {
    "memanto": {
      "command": "memanto-mcp",
      "env": {
        "MOORCHEH_API_KEY": "mch_xxxxxxxxxxxxxxxxxx",
        "MEMANTO_DEFAULT_AGENT_ID": "cursor-workspace"
      }
    }
  }
}

Client	Config path
Claude Desktop	~/Library/Application Support/Claude/claude_desktop_config.json (macOS) / %APPDATA%\Claude\claude_desktop_config.json (Windows)
Cursor	~/.cursor/mcp.json (or per-project .cursor/mcp.json)
Windsurf	~/.codeium/windsurf/mcp_config.json
Cline (VS Code)	~/.config/Code/User/globalStorage/cline.cline/settings/cline_mcp_settings.json
Continue	~/.continue/config.json → experimental.modelContextProtocolServers
Goose	~/.config/goose/config.yaml

Available tools

The server registers 7 memory tools by default. Set MEMANTO_EXPOSE_ADMIN=true to also expose 4 agent-management tools.

Memory tools (always on)

Tool	When the agent should call it
remember	Persist a single new fact/preference/decision/goal/instruction.
batch_remember	Persist up to 100 memories in one call (e.g. extracted from a document).
recall	Semantic search — always check here before asking the user to repeat stable info.
recall_recent	"What did we just decide?" — newest-first, no query needed.
recall_as_of	Point-in-time recall — "what did we know on 2025-11-01?"
recall_changed_since	Differential — "what's new since I last checked?"
answer	RAG: grounded LLM answer synthesized over the agent's memories.

Agent admin tools (opt-in)

Enabled when MEMANTO_EXPOSE_ADMIN=true:

Tool	Purpose
create_agent	Create a new memory namespace.
list_agents	List every agent the API key can see.
get_agent	Look up an agent's metadata.
delete_agent	Remove an agent's local metadata.

Memory types accepted by remember / batch_remember: fact, preference, goal, decision, artifact, learning, event, instruction, relationship, context, observation, commitment, error.

Provenance values: explicit_statement, inferred, corrected, validated, observed, imported.

Source attribution

source names who wrote a memory, so recall can be attributed and filtered per writer. It is open: user, agent, tool, system, or a specific writer such as cursor, codex, claude_code, mem0. Labels are limited to 64 letters, digits, ., _, or - so that #source:<value> stays a usable filter.

When a tool call omits source, the server attributes the write to the connected MCP client from the initialize handshake (cursor, codex, claude-ai, …), falling back to mcp-agent when the client sends no name. Two editors sharing one agent therefore stay distinguishable in recall without any extra configuration.

Configuration

All config is via environment variables (load order: process env → .env file in the working directory).

Variable	Required	Default	Description
MOORCHEH_API_KEY	yes	—	Moorcheh API key.
MEMANTO_DEFAULT_AGENT_ID	recommended	none	Default agent. When set, tool calls may omit agent_id.
MEMANTO_AGENT_PATTERN	no	tool	Pattern (support/project/tool) used when auto-creating the default agent.
MEMANTO_AGENT_AUTO_CREATE	no	true	Create the default agent on first use if missing. Explicit non-default agents must already exist.
MEMANTO_SESSION_DURATION_HOURS	no	server default (6)	Session lifetime in hours.
MEMANTO_EXPOSE_ADMIN	no	false	Register the 4 agent-management tools.
MEMANTO_MCP_TRANSPORT	no	stdio	stdio, sse, or streamable-http.
MEMANTO_MCP_HOST	no	127.0.0.1	Bind host for sse/http transports.
MEMANTO_MCP_PORT	no	8765	Bind port for sse/http transports.
MEMANTO_MCP_LOG_LEVEL	no	INFO	Log level (logs are always sent to stderr).

CLI flags (memanto-mcp --transport sse --port 9000) override env vars.

Running over HTTP / SSE

For remote clients or multi-process setups, run the server over a network transport:

# Streamable HTTP (recommended modern transport)
memanto-mcp --transport streamable-http --host 0.0.0.0 --port 8765

# Server-Sent Events (older, still widely supported)
memanto-mcp --transport sse --host 0.0.0.0 --port 8765

Then point your client at http://your-host:8765/mcp (or whatever path the chosen transport advertises). Pair with a reverse proxy + auth for production deployments — the server itself authenticates upstream to Moorcheh using your API key but does not authenticate inbound MCP clients.

How it works

┌──────────────┐    MCP/stdio    ┌──────────────────┐    Moorcheh API    ┌─────────────┐
│ Claude / IDE │ ──────────────► │  memanto-mcp     │ ────────────────► │   Moorcheh  │
│   (client)   │ ◄────────────── │  (this package)  │ ◄──────────────── │   Service   │
└──────────────┘    tool calls   └──────────────────┘    HTTPS+API key   └─────────────┘
                                          │
                                          └─ uses memanto.cli.client.SdkClient
                                             (same client the Memanto CLI uses)

• On startup, settings are validated; the API key is verified lazily on first tool call.
• On the first memory tool invocation for a given agent, the server ensures the agent exists (auto-creates if needed) and activates a JWT session. Sessions auto-renew before expiry, so long-running MCP connections never hit a session-expired error mid-conversation.
• The server intentionally keeps the session alive on shutdown: JWT sessions are TTL-bound and other Memanto clients (CLI, REST) may want to share them.

Programmatic embedding

If you're building a custom MCP host or wiring this server into a larger process, you can construct the FastMCP instance yourself:

from memanto_mcp import MCPServerSettings, build_server

settings = MCPServerSettings()  # reads env / .env
mcp = build_server(settings)

# Add your own tools alongside Memanto's, then run.
mcp.run(transport="stdio")

Troubleshooting

Symptom	Fix
configuration error: MOORCHEH_API_KEY is required	Set the env var in your MCP client config's env block.
Agent '…' does not exist and MEMANTO_AGENT_AUTO_CREATE is disabled	Either re-enable auto-create or call create_agent (admin tools) / memanto agent create <id> once.
Tools never appear in the client	Confirm the client supports MCP and the config path matches. Look at the client's MCP log: the server's stderr lines (prefixed memanto_mcp) will appear there on startup.
Garbled output in stdio mode	Something on your side is writing to stdout — that channel is reserved for JSON-RPC. Move logs to stderr. The server itself only writes to stderr.
Slow first call	Cold-start cost: SDK import + first session activation. Subsequent calls reuse the live session.

License

MIT — same as the Memanto project. See LICENSE.

Links

• Memanto — the memory agent itself
• Moorcheh — the no-indexing semantic DB underneath
• Model Context Protocol spec
• Anthropic MCP Python SDK